The generators in DIP are usually initialized with random weights. Unlike past convex strategies, the CNN-based algorithm in (REF ) is not guaranteed to converge to the global minimum of the cost function. The final solution will be dependent on the initialization. To improve performance and to reduce the computational complexity, we propose to initialize the \(\mathcal {G}_{\theta }\)  and \(\mathcal {G}_{\phi }\)  networks. In this work, we chose the SToRM [1]} data to initialize the network. We note from our past results that the SToRM approach yields improved results compared to the other state-of-the art algorithms [1]}, including low-rank methods.
In particular, we pretrain \(\mathcal {G}_{\theta }\)  and \(\mathcal {G}_{\phi }\)  generators independently using the SToRM factors, as
\(\theta _0 &=& \arg \min _{\theta }\Vert \mathcal {G}_{\theta }(\mathbf {U}_{0})-\mathbf {U}_{\rm SToRM}\Vert ^2,\\\lbrace \phi _0,\mathbf {Z}_0 \rbrace &=& \arg \min _{\phi ,\mathbf {Z}}\Vert \mathcal {G}_{\phi }(\mathbf {Z})-\mathbf {V}_{\rm SToRM}\Vert ^2.\) 
